Metagenomic analysis of a glacial ice core record from the contiguous United States

Metagenomic analysis of a glacial ice core record from the contiguous United States | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Metagenomic analysis of a glacial ice core record from the contiguous United States Brian Kvitko, Jason Wallace, Hanish Vasudev Desai, Heather F. Lavender, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5045654/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Glacial ice preserves time-sequenced records of preserved microbes, offering access to historic pre-anthropic metagenomes. As proof-of-concept, we three tested methods to recover ancient DNA from a ~250-year ice core from Wyoming's Upper Fremont Glacier for metagenomic sequencing. Direct amplification of filter-concentrated melt water (eDNA) was a simple and effective method for metagenomics. We observed higher microbial diversity, enriched nitrogen-cycling genes, and higher abundance of Nitrosopira, Rhodanobacter and Polaromonas sequences in 1790 CE samples compared to 1900-1961 CE samples. The documented microbial variation may be attributable to changes in climate and land use over the last two centuries. Metagenome glacial ice core ancient DNA eDNA Figures Figure 1 Figure 2 Full Text Accumulating glacial ice contains a time-sequenced record of atmospheric constituents from a time in the past, including viable and non-viable cells that remain preserved in the ice. Although small subunit ribosomal RNA amplicon sequencing has been a useful tool to characterize glacial ice assemblages [1-3], the limited phylogenetic and genetic resolution of this approach poses significant limitations for functional insights. Accessing the metagenome of glacial “fossils” can provide a uniquely powerful historical record of microbial genetics from varying climate conditions (e.g., [4]) and predate human activities that have transformed landscapes and land cover. In this study, we sought to validate techniques for recovery and sequencing of ancient genomes preserved in samples from an alpine glacier ice core that contained low biomass (~10 5 -10 7 cells L -1 ; Fig. 1B). The Wind River Range (Wyoming, USA) is one of few locations in the continental US where a detailed paleoecological glacial record is available [5-8]. Two ice cores recovered from the Upper Fremont Glacier (UFG) are archived by the National Science Foundation Ice Core Facility. The samples analyzed in this study were obtained from the ice core drilled in 1998 (43.1294444, -109.6163889) designated FRE98-4. Ice cores from the UFG contain an ~250-year record of climate and anthropogenic pollution for the contiguous United States that extends to the middle of the 18 th century (1746 to 1998 CE for FRE98-4) [5-8]. This coincides with the expansion of cultivated land area in the American West, as well as notable climatic events (i.e., termination of the Little Ice Age in 1870 CE and Dust Bowl drought of the 1930s) and increases in local air temperature (Fig. 1A). Analysis of ice samples from 28 depths using previously described methods [9] revealed a two orders of magnitude microbial cell concentration range (Fig. 1B). There is decanal to century scale variation in annual layer thickness in the FRE98-4 core. At our sampled depth resolution (17.5 to 90 cm), each sample represents a portion to annual year of deposition. The 70.9 mbs (meters below surface; 1940 CE) sample contained the highest observed cell concentration while lower concentrations were generally observed samples corresponding to the Little Ice Age. Ice core depths shallower than 117 mbs contained a record of biological aerosols from regional ecosystems during the early to late 20 th century. In samples originating from 1904 to 1966 CE (n=16), cell concentration is positively and significantly correlated to sample age (Spearman’s rank order correlation, r(14) = 0.603, p < 0.05). Although previous studies have shown positive correlations between cell and dust or black carbon concentrations in glacial ice [10-13], the variation in dust and black carbon data from FRE98-4 are dissimilar to the trend of increasing cell concentrations after 1904 CE (Fig. 1). Based on δ 18 O-H 2 O data (Fig. 1A), local air temperature since the termination of the Little Ice Age (1870 CE) to the early 1990s has increased by ~5 o C [5]. This period of warming coincided with large scale changes in land use and cover that accompanied the intensification of agriculture in the western United States (Fig. 1A). To examine variation in the microbial assemblages preserved with depth/age in the ice, we extracted DNA and sequenced metagenomes from six relatively large ice samples (650-920 g cleaned weight; Table S1). Based on top depths from 26.87-153.01 mbs the samples span 170 years (1961-1790 CE; Fig. 1). Aseptic sampling and cleaning of ice cores samples followed previously described methods and a mock sample of frozen sterile ultrapure water was processed as a control [9]. Melt water was filtered with Centricon-70 30 Kda devices by repeated 10-15 minute centrifugation cycles at 3,500 RCF using a swinging bucket rotor. We used three techniques to recover DNA. First, 10% of the recovered volumes were used directly as environmental DNA (eDNA). The remaining 90% volume was split evenly and processed using either the MP FastPrep FastDNA SPIN for Soil kit (FP) according to the manufacturer’s recommendations, or the Promega Wizard HMW DNA (HMW) for Gram negative bacteria with 80°C lysis treatment. Although, instead of ethanol precipitation, clarified lysate was filtered through a pre-washed Amicon Ultra-2 30 kDa filter and washed twice with TE buffer (pH 8.0) prior to retentate recovery. Samples were then amplified with the Takara PicoPLEX Single Cell WGA Kit v3 according to the manufacturer’s recommendations. Pure PicoPLEX reagent water was amplified as an additional negative control (PCRneg). DNA amplification yielded nine samples with fragment sizes >400 bp and DNA concentrations >10 ng/μL (Table S1). These were sequenced by SeqCenter using the on-bead Tagmentation Illumina DNA prep with the small shotgun metagenomic sequencing pipeline, targeting 6.5M 150-bp paired-end reads/sample. As expected, the control samples had low DNA concentrations (Table S1) and were sequenced using low-DNA input of the Nextera Flex for Enrichment library prep kit followed by MiSeq Illumina sequencing with a Nano flow cell, targeting 2M paired-end 250-bp reads/sample at the Georgia Genomics and Bioinformatics Core. For metagenomics analysis, adapter and quality trimming was performed with Trimmomatic [14], with a sliding window trim of 4 bp, minimum quality 30, leading and trailing quality cutoffs of 3, and a minimum length of 36 bp. Alpha and beta diversity were determined by rarefying samples to 140,000 reads, trimming to a maximum of 150 bp/read, and calculated with Krakentools [15]. Taxonomic classifications were performed with Kraken2 as well as Bracken at the genus level [16, 17]. All of the ice core samples show higher diversity than the negative controls (Fig. 2A), with a trend of lower diversity in the 1900-1948 CE samples. The controls clearly separated from the glacial samples based on Bray-Curtis ordination (Fig. 2B). The sample depths 116, 108, 69, and 51 mbs covering a ~50 year period (1900-1948 CE) had similar beta-diversity ordination although cell concentrations varied substantially over this time frame. The method of DNA extraction did not have an obvious effect on the recovered microbial community, with highly similar compositions inferred by each of the methods (Fig. 2B). The eDNA method produced the most samples suitable for sequencing (4 of 9) and required the least processing and thus may be a preferable choice for future metagenomic sequencing of glacial ice. Taxonomic profiles were calculated with Kaiju [18]. The most common and abundant taxa were bacteria in the Actinomycetota, especially Dietzia and Nesterenkonia (Fig. 2C). Over half of the most abundant genera were enriched in the oldest sample (153 mbs; 1790 CE). These include Dokdonella , Ginsengibacter , Hanamia , Nitrosospira , and Rhodanobacter , which on average, are present at >10-fold the abundance as in the other samples, and Gemmatirosa and Polaromonas , which were an average of 3 to 5-fold more abundant. Many species of Polaromonas are psychrophilic [19] and their presence could be consistent with the cooler Little Ice Age climate of 1790s. In addition, some ammonia oxidizing and denitrifying members of the Nitrosospira [20] and Rhodanobacter [21] are also known to be cold-adapted. Metagenome assemblies performed with IDBA-UD [22] were highly fragmented (Fig. S1). To filter out contaminating sequences in the assembled metagenomes, we aligned the reads from each to the 4 control samples using bwa [23]; any contigs with >10% coverage or >1x average depth from control assembles were removed. Functionality was determined with DRAM [24]. The most pronounced pattern was the enrichment of nitrogen-cycling genes in the 153 mbs samples (Fig. 2D) and may be related to the abundance of Nitrosospira and Rhodanobacter (Fig. 2A). Temporal patterns in antibiotic resistance genes were assessed by matching contigs against the Comprehensive Antibiotic Resistance Database [25]. We identified the efflux pump gene adeF , dihydrofolate reductase dfrB10 , and several vanY D,D-carboxypeptidase genes (Fig. S2). However,asthese genes can each participate in other cellular functions, their significance to antibiotic resistance is unclear. We identified an excess of plant sequences in the 116 mbs (1900 CE) sample (Fig. S3). While Kraken2 flagged a significant portion, (0.36% of total and 31% of plant sequences), as Cryptomeria japonica (Japanese Cedar), manual BLAST indicated sequences as similar to Pinus sp. (data not shown), which are widely distributed in the western United States [26]. This spike in plant signal coincides with low microbial cells count (Fig. 1B), moderate alpha diversity (Fig. 2), and the period when temperatures increased into the 21 st century [5] (Fig. 1A). We hypothesized that microbes immured in glacial ice would be enriched for ice nucleating activity (INA) genes due to their known role in bioprecipitation [27]. Metagenomic contigs were annotated using Prokka and a custom INA Hidden Markov Model (HMM) generated with hmmer [28] trained on twelve INA proteins aligned with muscle [29] was used to identify INA genes. A single INA gene was identified from the 153 eDNA sample that phylogenetically clusters with inaZ from Pseudomonas (Fig. S4). In conclusion, we present methods to extract, amplify, assemble and analyze metagenomic DNA from cells and eDNA preserved in glacial ice cores. Our results indicate that direct amplification of environmental DNA (the eDNA method) was reliable and the simplest approach for recovering metagenomics sequences. Further improvements to this method should focus on increasing the sample sequencing depths and contiguity. Declarations Conflict of Interest The authors declare no conflict of interest Data Availability Sequencing reads have been deposited with NCBI SRA under Bioproject PRJNA115358.1. The metagenomics analysis pipeline is fully available at https://github.com/wallacelab/paper-kvitko-relic-2024. Acknowledgements We would like to acknowledge Dr. Gi Yoon “Gina” Shin as well as Dr. Magdy Alabady from the University of Georgia GGBC for their assistance and advice with sample processing, sequencing, and analysis. We would also like to acknowledge the National Science Foundation Ice Core Facility. Funding This work was supported in part by NSF awards to B.C.C. (RAINS, 1241161 and 1643288) and an internal award to B.K. from the UGA Office of the Vice President for Research. References Zhong, Z.-P., et al., Glacier ice archives nearly 15,000-year-old microbes and phages. Microbiome, 2021. 9 : p. 1-23. Sherpa, M.T., et al., Exploration of microbial diversity of Himalayan glacier moraine soil using 16S amplicon sequencing and phospholipid fatty acid analysis approaches. Current Microbiology, 2021. 78 : p. 78-85. Segawa, T., et al., Altitudinal changes in a bacterial community on Gulkana Glacier in Alaska. Microbes and environments, 2010. 25 (3): p. 171-182. Zhong, Z.-P., et al., Glacier-preserved Tibetan Plateau viral community probably linked to warm–cold climate variations. Nature Geoscience, 2024: p. 1-8. Naftz, D.L., et al., Ice core evidence of rapid air temperature increases since 1960 in alpine areas of the Wind River Range, Wyoming, United States. Journal of Geophysical Research: Atmospheres, 2002. 107 (D13): p. ACL 3-1-ACL 3-16. Schuster, P.F., et al., Atmospheric mercury deposition during the last 270 years: a glacial ice core record of natural and anthropogenic sources. Environmental science & technology, 2002. 36 (11): p. 2303-2310. Aarons, S., et al., Ice core record of dust sources in the western United States over the last 300 years. Chemical Geology, 2016. 442 : p. 160-173. Chellman, N., et al., Reassessment of the Upper Fremont Glacier Ice-Core Chronologies by Synchronizing of Ice-Core-Water Isotopes to a Nearby Tree-Ring Chronology. Environ Sci Technol, 2017. 51 (8): p. 4230-4238. Christner, B.C., et al., Glacial ice cores: a model system for developing extraterrestrial decontamination protocols. Icarus, 2005. 174 (2): p. 572-584. Yao, T., et al., Bacteria variabilities in a Tibetan ice core and their relations with climate change. Global Biogeochemical Cycles, 2008. 22 (4). Liu, Y., et al., Bacterial responses to environmental change on the T ibetan P lateau over the past half century. Environmental Microbiology, 2016. 18 (6): p. 1930-1941. Zhang, S., et al., Culturable bacteria in Himalayan glacial ice in response to atmospheric circulation. Biogeosciences, 2007. 4 (1): p. 1-9. Christner, B.C., et al., Recovery and identification of viable bacteria immured in glacial ice. Icarus, 2000. 144 (2): p. 479-485. Bolger, A.M., M. Lohse, and B. Usadel, Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 2014. 30 (15): p. 2114-2120. Lu, J., et al., Metagenome analysis using the Kraken software suite. Nature protocols, 2022. 17 (12): p. 2815-2839. Lu, J., et al., Bracken: estimating species abundance in metagenomics data. PeerJ Computer Science, 2017. 3 : p. e104. Wood, D.E., J. Lu, and B. Langmead, Improved metagenomic analysis with Kraken 2. Genome biology, 2019. 20 : p. 1-13. Menzel, P., K.L. Ng, and A. Krogh, Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nature communications, 2016. 7 (1): p. 11257. Darcy, J.L., et al., Global distribution of Polaromonas phylotypes-evidence for a highly successful dispersal capacity. PLoS One, 2011. 6 (8): p. e23742. Sanders, T., et al., Cold adapted Nitrosospira sp.: a potential crucial contributor of ammonia oxidation in cryosols of permafrost-affected landscapes in Northeast Siberia. Microorganisms, 2019. 7 (12): p. 699. Ferguson, D.K., et al., Natural attenuation of spilled crude oil by cold-adapted soil bacterial communities at a decommissioned High Arctic oil well site. Science of The Total Environment, 2020. 722 : p. 137258. Peng, Y., et al., IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics, 2012. 28 (11): p. 1420-1428. Li, H. and R. Durbin, Fast and accurate short read alignment with Burrows–Wheeler transform. bioinformatics, 2009. 25 (14): p. 1754-1760. Shaffer, M., et al., DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic acids research, 2020. 48 (16): p. 8883-8900. McArthur, A.G., et al., The comprehensive antibiotic resistance database. Antimicrobial agents and chemotherapy, 2013. 57 (7): p. 3348-3357. Mitchell, J.E. and T.C. Roberts Jr. Distribution of pinyon-juniper in the western United States . in Proceedings of the Ecology and Management of Pinyon-juniper Communities Within the Interior West. USDA Forest Service Proceedings RMRS-P-9. Rocky Mountain Research Station, Fort Collins, CO . 1999. Morris, C.E., et al., Bioprecipitation: a feedback cycle linking Earth history, ecosystem dynamics and land use through biological ice nucleators in the atmosphere. Global change biology, 2014. 20 (2): p. 341-351. Potter, S.C., et al., HMMER web server: 2018 update. Nucleic acids research, 2018. 46 (W1): p. W200-W204. Edgar, R.C., MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics, 2004. 5 : p. 113. Klein Goldewijk, K., et al., Anthropogenic land use estimates for the Holocene–HYDE 3.2. Earth System Science Data, 2017. 9 (2): p. 927-953. Hyatt, D., et al., Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics, 2010. 11 : p. 119. Price, M.N., P.S. Dehal, and A.P. Arkin, FastTree 2–approximately maximum-likelihood trees for large alignments. PloS one, 2010. 5 (3): p. e9490. Yu, G., et al., ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods in Ecology and Evolution, 2017. 8 (1): p. 28-36. Additional Declarations No competing interests reported. Supplementary Files TableS1.V1.xlsx Table S1: FRE98-4 ice core sample depths and processing measurements. SuppFigure1Nxgraphsmetagenomeassemblyquality.png Figure S1: Nx graphs showing contiguity of the assembled metagenomes. Each line represents the shortest contig length (Y axis) for each percentage (X axis) of the genome. All assemblies have a few large contigs and many small ones, indicating they are highly fragmented. SuppFigure2CARDpredictions.png Figure S2: Antibiotic resistance predictions. The presence of antibiotic resistance genes was predicted with the Comprehensive Antibiotic Resistance Database (CARD) [25]. SuppFigure3PlantAnimalSeqs.png Figure S3: Plant sequences in each sample. The amount of sequences assigned to different plant families, as determined by Kraken2 [17]. Only families that have at least 0.02% of reads in at least one sample are shown. SuppFig4UFGNPTree.jpg Figure S4: Phylogenetic tree of INA proteins from UFG samples. Unrooted circular phylogenetic tree of protein sequences recovered from INA proteins. Branches supported with greater than 80% support are highlighted with blue dots. Circles outside of the tree represent information about the INA proteins. The inner circle annotates dataset from which the INA protein was recovered. The outer circle represents that genus of the organism encoding the INA protein. The complete bacterial genomes from RefSeq and the metagenomic assemblies were annotated with prodigal [31] . Amino acid sequences from the RefSeq database were classified using the custom INA HMM. Hits that had e-values of less than 1e-30 and were longer than 500 amino acids were considered to be significant hits. A phylogenetic tree was constructed from positive hits by aligning the amino acid sequences using muscle followed by phylogenetic tree construction fasttree [32]. The fasttree phylogenetic tree was constructed using the Whelan and Goldman (WAG) model for protein evolution (Whelan and Goldman) and the gamma distribution. Branches supported with greater than 80% support are highlighted with blue dots. Circles outside of the tree represent information about the INA proteins. Reliability of branch points was determined using the Shimodiara-Hasegawa test. Phylogenetic trees were visualized using ggtree [33]. Branches supported with greater than 80% support are highlighted with blue dots. Circles outside of the tree represent information about the INA proteins. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5045654","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":355405327,"identity":"d0bdb29f-a907-4123-9a78-fad79e59b447","order_by":0,"name":"Brian Kvitko","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACAx4GhgNAKAfhshGr5cABBmPStAD1MCQ2EK3FnOeM4eEPZ2zSN5xf/IDhQ9lhwlose3sMDhy4kZa74cYzA8YZ54jQYnCeLeHAgQ+HgVoOGDDztpGgJd3gxvEPzH+J0nK2GRhgNw4nGJzvMWBmJEaLZc/hAwfOnEkznHmDp+Bgz7l0wlrMeRKbP1Qcs5HnO39844MfZdaEtSCARAIwEZAG+EnVMApGwSgYBSMGAACMC0oWlPPbaAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Georgia","correspondingAuthor":true,"prefix":"","firstName":"Brian","middleName":"","lastName":"Kvitko","suffix":""},{"id":355405328,"identity":"baee709d-af49-4192-8fab-788a701f77a5","order_by":1,"name":"Jason Wallace","email":"","orcid":"","institution":"University of Georgia","correspondingAuthor":false,"prefix":"","firstName":"Jason","middleName":"","lastName":"Wallace","suffix":""},{"id":355405329,"identity":"264ac964-d2aa-4708-b46d-0fb0e69d1553","order_by":2,"name":"Hanish Vasudev Desai","email":"","orcid":"","institution":"University of Georgia","correspondingAuthor":false,"prefix":"","firstName":"Hanish","middleName":"Vasudev","lastName":"Desai","suffix":""},{"id":355405330,"identity":"7a895a62-57a5-4792-a1ea-ea3d557a74ab","order_by":3,"name":"Heather F. 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(A)\u0026nbsp; Climatic and environmental data. The data for δ\u003csup\u003e18\u003c/sup\u003eO-H\u003csub\u003e2\u003c/sub\u003eO are from Naftz et al. 2002 [5], dust are from Aarons et al. 2016 [7, 8], black carbon are from Chellman et al. 2017 [7, 8], and average cropland are from Goldewijk et al. 2017 [30].(B) Concentration of DNA-containing cells L\u003csup\u003e-1\u003c/sup\u003e was estimated by epifluorescence staining of SYBR Gold-stained cells from 28 ice core samples [9]. The six sample depths used for metagenomics study are marked with dashed red lines.\u003c/p\u003e","description":"","filename":"UFGFig1v3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5045654/v1/04e5f0d2a2efadf13b8dd917.jpg"},{"id":64833153,"identity":"063255d6-f11d-4052-8457-3696d15afb2e","added_by":"auto","created_at":"2024-09-19 10:04:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":379938,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetagenomic analysis of FRE98 samples and controls. \u003c/strong\u003eSamples are labeled with their depth (or Control, “C”) and extraction method; “PCRneg” was the reagent control for the PicoPLEX Single Cell WGA Kit. (A) Alpha diversity (Shannon index) of each sample as determined with Krakentools [15]. All actual samples have higher diversity than the negative controls, and diversity among samples follows a U-shaped curve, with the most diversity at the beginning and end of the depth sequence. (B) Principal coordinate plot of samples baked on Bray-Curtis distances among them. Controls clearly cluster apart from environmental samples, while the samples generally cluster by depth. (C) Taxonomic profile of glacial samples as determined with Kaiju [18] and collapsed at the genus level. (D) Presence/absence of nitrogen metabolism genes in metagenome-assembled contigs, as determined by DRAM [24].\u003c/p\u003e","description":"","filename":"Fig25aresultsfigure.COMBINED.png","url":"https://assets-eu.researchsquare.com/files/rs-5045654/v1/958d298deecfe102a04dd73d.png"},{"id":64834449,"identity":"308c7378-9dfe-450e-a0e8-f5d612ce54d9","added_by":"auto","created_at":"2024-09-19 10:20:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":932753,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5045654/v1/3d0d95c9-4631-44b4-ab08-12d62aee67c5.pdf"},{"id":64832460,"identity":"cd134e85-aeff-4e8c-a3ff-751799b5bb14","added_by":"auto","created_at":"2024-09-19 09:56:48","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11360,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1:\u003c/strong\u003e FRE98-4 ice core sample depths and processing measurements.\u003c/p\u003e","description":"","filename":"TableS1.V1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5045654/v1/7a27664baf51fcaeb1f0a151.xlsx"},{"id":64833156,"identity":"d56c25d5-f0a4-492b-afce-20b3a83fbc3c","added_by":"auto","created_at":"2024-09-19 10:04:48","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":296841,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1: Nx graphs showing contiguity of the assembled metagenomes.\u003c/strong\u003e Each line represents the shortest contig length (Y axis) for each percentage (X axis) of the genome. All assemblies have a few large contigs and many small ones, indicating they are highly fragmented.\u003c/p\u003e","description":"","filename":"SuppFigure1Nxgraphsmetagenomeassemblyquality.png","url":"https://assets-eu.researchsquare.com/files/rs-5045654/v1/a79c1349cb2633714a769edb.png"},{"id":64832461,"identity":"76cb0b1c-5ae6-4964-bb2a-d53f66e5c8f0","added_by":"auto","created_at":"2024-09-19 09:56:48","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":88086,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2: Antibiotic resistance predictions.\u003c/strong\u003e The presence of antibiotic resistance genes was predicted with the Comprehensive Antibiotic Resistance Database (CARD) [25].\u003c/p\u003e","description":"","filename":"SuppFigure2CARDpredictions.png","url":"https://assets-eu.researchsquare.com/files/rs-5045654/v1/b5cf198b4b70f6fc47cfa6f5.png"},{"id":64834448,"identity":"4d241a1a-f36b-4e94-8cfe-3a509d8079b6","added_by":"auto","created_at":"2024-09-19 10:20:48","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":171287,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S3: Plant sequences in each sample.\u003c/strong\u003e The amount of sequences assigned to different plant families, as determined by Kraken2 [17]. Only families that have at least 0.02% of reads in at least one sample are shown.\u003c/p\u003e","description":"","filename":"SuppFigure3PlantAnimalSeqs.png","url":"https://assets-eu.researchsquare.com/files/rs-5045654/v1/0eaa0cf820e99b56e851f31a.png"},{"id":64833924,"identity":"bb9c26f7-e895-40e7-967d-695316793708","added_by":"auto","created_at":"2024-09-19 10:12:48","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1244973,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S4: Phylogenetic tree of INA proteins from UFG samples.\u003c/strong\u003e Unrooted circular phylogenetic tree of protein sequences recovered from INA proteins. Branches supported with greater than 80% support are highlighted with blue dots. Circles outside of the tree represent information about the INA proteins. The inner circle annotates dataset from which the INA protein was recovered. The outer circle represents that genus of the organism encoding the INA protein. The complete bacterial genomes from RefSeq and the metagenomic assemblies were annotated with prodigal [31] . Amino acid sequences from the RefSeq database were classified using the custom INA HMM. Hits that had e-values of less than 1e-30 and were longer than 500 amino acids were considered to be significant hits. A phylogenetic tree was constructed from positive hits by aligning the amino acid sequences using muscle followed by phylogenetic tree construction fasttree [32]. The fasttree phylogenetic tree was constructed using the Whelan and Goldman (WAG) model for protein evolution (Whelan and Goldman) and the gamma distribution. Branches supported with greater than 80% support are highlighted with blue dots. Circles outside of the tree represent information about the INA proteins. Reliability of branch points was determined using the Shimodiara-Hasegawa test. Phylogenetic trees were visualized using ggtree [33]. Branches supported with greater than 80% support are highlighted with blue dots. Circles outside of the tree represent information about the INA proteins.\u003c/p\u003e","description":"","filename":"SuppFig4UFGNPTree.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5045654/v1/515f91e95f7b98194e677dfd.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metagenomic analysis of a glacial ice core record from the contiguous United States","fulltext":[{"header":"Full Text","content":"\u003cp\u003eAccumulating glacial ice contains a time-sequenced record of atmospheric constituents from a time in the past, including viable and non-viable cells that remain preserved in the ice. Although small subunit ribosomal RNA amplicon sequencing has been a useful tool to characterize glacial ice assemblages\u0026nbsp;[1-3], the limited phylogenetic and genetic resolution of this approach poses significant limitations for functional insights. Accessing the metagenome of glacial “fossils” can provide a uniquely powerful historical record of microbial genetics from varying climate conditions (e.g.,\u0026nbsp;[4]) and predate human activities that have transformed landscapes and land cover. In this study, we sought to validate techniques for recovery and sequencing of ancient genomes preserved in samples from an alpine glacier ice core that contained low biomass (~10\u003csup\u003e5\u003c/sup\u003e-10\u003csup\u003e7\u003c/sup\u003e cells L\u003csup\u003e-1\u003c/sup\u003e; Fig. 1B).\u003c/p\u003e\n\u003cp\u003eThe Wind River Range (Wyoming, USA) is one of few locations in the continental US where a detailed paleoecological glacial record is available\u0026nbsp;[5-8]. Two ice cores recovered from the Upper Fremont Glacier (UFG) are archived by the National Science Foundation Ice Core Facility. The samples analyzed in this study were obtained from the ice core drilled in 1998 (43.1294444, -109.6163889) designated FRE98-4. Ice cores from the UFG contain an ~250-year record of climate and anthropogenic pollution for the contiguous United States that extends to the middle of the 18\u003csup\u003eth\u003c/sup\u003e century (1746 to 1998 CE for FRE98-4)\u0026nbsp;[5-8]. This coincides with the expansion of cultivated land area in the American West, as well as notable climatic events (i.e., termination of the Little Ice Age in 1870 CE and Dust Bowl drought of the 1930s) and increases in local air temperature (Fig. 1A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnalysis of ice samples from 28 depths using previously described methods\u0026nbsp;[9]\u0026nbsp;revealed a two orders of magnitude microbial cell concentration range (Fig. 1B). There is decanal to century scale variation in annual layer thickness in the FRE98-4 core. At our sampled depth resolution (17.5 to 90 cm), each sample represents a portion to annual year of deposition. The 70.9 mbs (meters below surface; 1940 CE) sample contained the highest observed cell concentration while lower concentrations were generally observed samples corresponding to the Little Ice Age. Ice core depths shallower than 117 mbs contained a record of biological aerosols from regional ecosystems during the early to late 20\u003csup\u003eth\u003c/sup\u003e century. In samples originating from 1904 to 1966 CE (n=16), cell concentration is positively and significantly correlated to sample age (Spearman’s rank order correlation, r(14) = 0.603, p \u0026lt; 0.05). Although previous studies have shown positive correlations between cell and dust or black carbon concentrations in glacial ice\u0026nbsp;[10-13], the variation in dust and black carbon data from FRE98-4 are dissimilar to the trend of increasing cell concentrations after 1904 CE (Fig. 1). Based on δ\u003csup\u003e18\u003c/sup\u003eO-H\u003csub\u003e2\u003c/sub\u003eO data (Fig. 1A), local air temperature since the termination of the Little Ice Age (1870 CE) to the early 1990s has increased by ~5\u003csup\u003eo\u003c/sup\u003eC\u0026nbsp;[5]. This period of warming coincided with large scale changes in land use and cover that accompanied the intensification of agriculture in the western United States (Fig. 1A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo examine variation in the microbial assemblages preserved with depth/age in the ice, we extracted DNA and sequenced metagenomes from six relatively large ice samples (650-920 g cleaned weight; Table S1). Based on top depths from 26.87-153.01 mbs the samples span 170 years (1961-1790 CE; Fig. 1). Aseptic sampling and cleaning of ice cores samples followed previously described methods and a mock sample of frozen sterile ultrapure water was processed as a control\u0026nbsp;[9]. Melt water was filtered with Centricon-70 30 Kda devices by repeated 10-15 minute centrifugation cycles at 3,500 RCF using a swinging bucket rotor. We used three techniques to recover DNA. First, 10% of the recovered volumes were used directly as environmental DNA (eDNA). The remaining 90% volume was split evenly and processed using either the MP FastPrep FastDNA SPIN for Soil kit (FP) according to the manufacturer’s recommendations, or the Promega Wizard HMW DNA (HMW) for Gram negative bacteria with 80°C lysis treatment. Although, instead of ethanol precipitation, clarified lysate was filtered through a pre-washed Amicon Ultra-2 30 kDa filter and washed twice with TE buffer (pH 8.0) prior to retentate recovery. Samples were then amplified with the Takara PicoPLEX Single Cell WGA Kit v3 according to the manufacturer’s recommendations. Pure PicoPLEX reagent water was amplified as an additional negative control (PCRneg). DNA amplification yielded nine samples with fragment sizes \u0026gt;400 bp and DNA concentrations \u0026gt;10 ng/μL (Table S1). These were sequenced by SeqCenter using the on-bead Tagmentation Illumina DNA prep with the small shotgun metagenomic sequencing pipeline, targeting 6.5M 150-bp paired-end reads/sample. As expected, the control samples had low DNA concentrations (Table S1) and were sequenced using low-DNA input of the Nextera Flex for Enrichment library prep kit followed by MiSeq Illumina sequencing with a Nano flow cell, targeting 2M paired-end 250-bp reads/sample at the Georgia Genomics and Bioinformatics Core.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor metagenomics analysis, adapter and quality trimming was performed with Trimmomatic\u0026nbsp;[14], with a sliding window trim of 4 bp, minimum quality 30, leading and trailing quality cutoffs of 3, and a minimum length of 36 bp. Alpha and beta diversity were determined by rarefying samples to 140,000 reads, trimming to a maximum of 150 bp/read, and calculated with Krakentools\u0026nbsp;[15]. Taxonomic classifications were performed with Kraken2 as well as Bracken at the genus level\u0026nbsp;[16, 17]. All of the ice core samples show higher diversity than the negative controls (Fig. 2A), with a trend of lower diversity in the 1900-1948 CE samples. The controls clearly separated from the glacial samples based on Bray-Curtis ordination (Fig. 2B). The sample depths 116, 108, 69, and 51 mbs covering a ~50 year period (1900-1948 CE) had similar beta-diversity ordination although cell concentrations varied substantially over this time frame. The method of DNA extraction did not have an obvious effect on the recovered microbial community, with highly similar compositions inferred by each of the methods (Fig. 2B). The eDNA method produced the most samples suitable for sequencing (4 of 9) and required the least processing and thus may be a preferable choice for future metagenomic sequencing of glacial ice.\u003c/p\u003e\n\u003cp\u003eTaxonomic profiles were calculated with Kaiju\u0026nbsp;[18]. The most common and abundant taxa were bacteria in the Actinomycetota, especially \u003cem\u003eDietzia\u003c/em\u003e and \u003cem\u003eNesterenkonia\u003c/em\u003e (Fig. 2C). Over half of the most abundant genera were enriched in the oldest sample (153 mbs; 1790 CE). These include \u003cem\u003eDokdonella\u003c/em\u003e, \u003cem\u003eGinsengibacter\u003c/em\u003e, \u003cem\u003eHanamia\u003c/em\u003e, \u003cem\u003eNitrosospira\u003c/em\u003e, and \u003cem\u003eRhodanobacter\u003c/em\u003e, which on average, are present at \u0026gt;10-fold the abundance as in the other samples, and \u003cem\u003eGemmatirosa\u003c/em\u003e and \u003cem\u003ePolaromonas\u003c/em\u003e, which were an average of 3 to 5-fold more abundant. Many species of \u003cem\u003ePolaromonas\u003c/em\u003e are psychrophilic\u0026nbsp;[19]\u0026nbsp;and their presence could be consistent with the cooler Little Ice Age climate of 1790s. In addition, some ammonia oxidizing and denitrifying members of the \u003cem\u003eNitrosospira\u003c/em\u003e [20]\u0026nbsp;and \u003cem\u003eRhodanobacter\u0026nbsp;\u003c/em\u003e[21]\u0026nbsp;are also known to be cold-adapted.\u003c/p\u003e\n\u003cp\u003eMetagenome assemblies performed with IDBA-UD\u0026nbsp;[22]\u0026nbsp;were highly fragmented (Fig. S1). To filter out contaminating sequences in the assembled metagenomes, we aligned the reads from each to the 4 control samples using bwa\u0026nbsp;[23]; any contigs with \u0026gt;10% coverage or \u0026gt;1x average depth from control assembles were removed. Functionality was determined with DRAM\u0026nbsp;[24]. \u0026nbsp;The most pronounced pattern was the enrichment of nitrogen-cycling genes in the 153 mbs samples (Fig. 2D) and may be related to the abundance of \u003cem\u003eNitrosospira\u0026nbsp;\u003c/em\u003eand \u003cem\u003eRhodanobacter\u003c/em\u003e (Fig. 2A). Temporal patterns in antibiotic resistance genes were assessed by matching contigs against the Comprehensive Antibiotic Resistance Database\u0026nbsp;[25]. We identified the efflux pump gene \u003cem\u003eadeF\u003c/em\u003e, dihydrofolate reductase \u003cem\u003edfrB10\u003c/em\u003e, and several \u003cem\u003evanY\u0026nbsp;\u003c/em\u003eD,D-carboxypeptidase genes (Fig. S2). However,asthese genes can each participate in other cellular functions, their significance to antibiotic resistance is unclear.\u003c/p\u003e\n\u003cp\u003eWe identified an excess of plant sequences in the 116 mbs (1900 CE) sample (Fig. S3). While Kraken2 flagged a significant portion, (0.36% of total and 31% of plant sequences), as \u003cem\u003eCryptomeria japonica\u003c/em\u003e (Japanese Cedar), manual BLAST indicated sequences as similar to \u003cem\u003ePinus\u003c/em\u003e sp. (data not shown), which are widely distributed in the western United States\u0026nbsp;[26]. This spike in plant signal coincides with low microbial cells count (Fig. 1B), moderate alpha diversity (Fig. 2), and the period when temperatures increased into the 21\u003csup\u003est\u003c/sup\u003e century\u0026nbsp;[5]\u0026nbsp;(Fig. 1A).\u003c/p\u003e\n\u003cp\u003eWe hypothesized that microbes immured in glacial ice would be enriched for ice nucleating activity (INA) genes due to their known role in bioprecipitation\u0026nbsp;[27]. Metagenomic contigs were annotated using Prokka and a custom INA Hidden Markov Model (HMM) generated with hmmer\u0026nbsp;[28]\u0026nbsp;trained on twelve INA proteins aligned with muscle\u0026nbsp;[29]\u0026nbsp;was used to identify INA genes. A single INA gene was identified from the 153 eDNA sample that phylogenetically clusters with \u003cem\u003einaZ\u003c/em\u003e from \u003cem\u003ePseudomonas\u0026nbsp;\u003c/em\u003e(Fig. S4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, we present methods to extract, amplify, assemble and analyze metagenomic DNA from cells and eDNA preserved in glacial ice cores. Our results indicate that direct amplification of environmental DNA (the eDNA method) was reliable and the simplest approach for recovering metagenomics sequences. Further improvements to this method should focus on increasing the sample sequencing depths and contiguity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequencing reads have been deposited with NCBI SRA under Bioproject PRJNA115358.1. The metagenomics analysis pipeline is fully available at https://github.com/wallacelab/paper-kvitko-relic-2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge Dr. Gi Yoon \u0026ldquo;Gina\u0026rdquo; Shin as well as Dr. Magdy Alabady from the University of Georgia GGBC for their assistance and advice with sample processing, sequencing, and analysis. We would also like to acknowledge the National Science Foundation Ice Core Facility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported in part by NSF awards to B.C.C. 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Arkin, \u003cem\u003eFastTree 2\u0026ndash;approximately maximum-likelihood trees for large alignments.\u003c/em\u003e PloS one, 2010. \u003cstrong\u003e5\u003c/strong\u003e(3): p. e9490.\u003c/li\u003e\n\u003cli\u003eYu, G., et al., \u003cem\u003eggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data.\u003c/em\u003e Methods in Ecology and Evolution, 2017. \u003cstrong\u003e8\u003c/strong\u003e(1): p. 28-36.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Metagenome, glacial ice core, ancient DNA, eDNA, ","lastPublishedDoi":"10.21203/rs.3.rs-5045654/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5045654/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlacial ice preserves time-sequenced records of preserved microbes, offering access to historic pre-anthropic metagenomes. As proof-of-concept, we three tested methods to recover ancient DNA from a ~250-year ice core from Wyoming's Upper Fremont Glacier for metagenomic sequencing. Direct amplification of filter-concentrated melt water (eDNA) was a simple and effective method for metagenomics. We observed higher microbial diversity, enriched nitrogen-cycling genes, and higher abundance of \u003cem\u003eNitrosopira, Rhodanobacter\u003c/em\u003e and \u003cem\u003ePolaromonas\u003c/em\u003e sequences in 1790 CE samples compared to 1900-1961 CE samples. The documented microbial variation may be attributable to changes in climate and land use over the last two centuries.\u003c/p\u003e","manuscriptTitle":"Metagenomic analysis of a glacial ice core record from the contiguous United States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-19 09:56:44","doi":"10.21203/rs.3.rs-5045654/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5a4b859e-5e4b-4446-8732-bde4f45a0726","owner":[],"postedDate":"September 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-19T09:56:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-19 09:56:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5045654","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5045654","identity":"rs-5045654","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}